scholarly journals Pronóstico de caudales medios mensuales del rio caplina, aplicando redes neuronales artificiales (rna) y modelo autorregresivo periódico de primer orden par (1)

2019 ◽  
pp. 69-72
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stochastic Models ◽  
Autoregressive Model ◽  
Model Development ◽  
Water Current ◽  
Long Time ◽  
Artificial Neural ◽  
Monthly Discharge ◽  
Del Rio

Pronóstico de caudales medios mensuales del rio caplina, aplicando redes neuronales artificiales (rna) y modelo autorregresivo periódico de primer orden par (1) Forecast for mean monthly discharge of the caplina river, by applying artificial neural network (rna) and periodic Autoregressive model par (1) Pino Vargas Edwin, Siña Espinoza Luis, Román Arce Carmen Programa de Doctorado en Recursos Hídricos / U.N.Agraria La Molina, Lima Perú, [email protected] Universidad Nacional Jorge Basadre G. Tacna, [email protected] Universidad Nacional Jorge Basadre G. Tacna, [email protected] DOI: https://doi.org/10.33017/RevECIPeru2011.0025/ RESUMEN El rio Caplina es el principal tributario de la cuenca hidrográfica del mismo nombre; tiene una extensión de 4 239,09 km2, esto hace que sea una de las principales fuentes de abastecimiento de agua para distintos usos en la ciudad de Tacna. Por esta razón diversas entidades se han interesado en conocer la disponibilidad hídrica actual y futura del rio Caplina, ya que conocer dichos valores es de fundamental importancia para el planeamiento y manejo de los sistemas de recursos hídricos. Los modelos estocásticos han sido durante largo tiempo, la alternativa más común en la predicción de caudales. Actualmente, las herramientas de computación inteligente como las redes neuronales artificiales, especialmente las redes multi-capas con algoritmo de retro-propagación. En este contexto, la actual investigación centro sus esfuerzos en la aplicación de las redes neuronales a la predicción de los caudales medios mensuales del río Caplina-Estación Bocatoma Calientes, desarrollo de modelos de redes neuronales a partir de datos de caudales, precipitación y evaporación, así como la evaluación de la capacidad de desempeño frente a modelos estocásticos. De esta manera, se desarrollaron 10 modelos de redes neuronales artificiales con distintas arquitecturas, cuyo entrenamiento se realizo con un primer subconjunto de datos correspondientes al periodo 1939 – 1999, y su validación con un segundo subconjunto de datos del periodo 2000 – 2006. Los modelos de redes neuronales artificiales mostraron comparativamente mejor desempeño en materia de predicción frente a un modelo autorregresivo periódico de primer orden PAR (1). Descriptores: Cuenca Caplina, Redes Neuronales Artificiales, Series de Tiempo. ABSTRACT Caplina river is the main tributary of the hydrographic basin of the same name, It has an extension of 4 239,09 km2, because of this reason it is one of the principal sources of water supply for different uses in Tacna's city. For this reason diverse entities have been interested in knowing the water current and future availability of the river Caplina, because know the above mentioned values performs is the fundamental importance for the planning and managing of the systems of water resources. The stochastic models have been during long time, the most common alternative in the prediction of flows. Nowadays, the tools of intelligent computation like the artificial neural networks, specially the networks you multi-geld with algorithm of retro-spread. In this context, the current investigation center his efforts on the application of the neural networks to the prediction of the average monthly flows of the river Caplina-station Bocatoma Calientes, model development of neural networks from information of flows, rainfall and evaporation, as well as the evaluation of the capacity of performance opposite to stochastic models. So, 10 models of artificial neural networks were developed with different architectures, which training was realize with the first subset of information corresponding to the period 1939 - 1999, and his validation with the second subset of information of the period 2000 - 2006. The models of artificial neural networks showed comparatively better performance as for prediction opposite to a periodic autoregressive model of the first order PAR (1). Keywords: Caplina Basin, artificial neural networks, Series of Time.

scholarly journals Applicability of Artificial Neural Networks to Predict Mechanical and Permeability Properties of Volcanic Scoria-Based Concrete

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Aref M. al-Swaidani ◽  
Waed T. Khwies
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Supplementary Cementitious Material ◽  
Natural Pozzolans ◽  
Long Time ◽  
Ann Models ◽  
Artificial Neural ◽  
And Performance ◽  
And Training

Numerous volcanic scoria (VS) cones are found in many places worldwide. Many of them have not yet been investigated, although few of which have been used as a supplementary cementitious material (SCM) for a long time. The use of natural pozzolans as cement replacement could be considered as a common practice in the construction industry due to the related economic, ecologic, and performance benefits. In the current paper, the effect of VS on the properties of concrete was investigated. Twenty-one concrete mixes with three w/b ratios (0.5, 0.6, and 0.7) and seven replacement levels of VS (0%, 10%, 15%, 20%, 25%, 30%, and 35%) were produced. The investigated concrete properties were the compressive strength, the water permeability, and the concrete porosity. Artificial neural networks (ANNs) were used for prediction of the investigated properties. Feed-forward backpropagation neural networks have been used. The ANN models have been established by incorporation of the laboratory experimental data and by properly choosing the network architecture and training processes. This study shows that the use of ANN models provided a more accurate tool to capture the effects of five parameters (cement content, volcanic scoria content, water content, superplasticizer content, and curing time) on the investigated properties. This prediction makes it possible to design VS-based concretes for a desired strength, water impermeability, and porosity at any given age and replacement level. Some correlations between the investigated properties were derived from the analysed data. Furthermore, the sensitivity analysis showed that all studied parameters have a strong effect on the investigated properties. The modification of the microstructure of VS-based cement paste has been observed, as well.

Underwriting Automobile Insurance Using Artificial Neural Networks

2011 ◽  
pp. 3865-3870
Author(s):  
Fred Kitchens
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Automobile Insurance ◽  
Long Time ◽  
Artificial Neural ◽  
Pencil And Paper

For hundreds of years, actuaries used pencil and paper to perform their statistical analysis It was a long time before they had the help of a mechanical adding machine. Only recently have they had the benefit of computers. As recently as 1981, computers were not considered important to the process of insurance underwriting. Leading experts in insurance underwriting believed that the judgment factor involved in the underwriting process was too complex for any computer to handle as effectively as a human underwriter (Holtom, 1981). Recent research in the application of technology to the underwriting process has shown that Holtom’s statement may no longer hold true (Gaunt, 1972; Kitchens, 2000; Rose, 1986). The time for computers to take on an important role in the insurance underwriting process may be upon us. The author intends to illustrate the applicability of artificial neural networks to the insurance underwriting process.

scholarly journals Modelling and prediction of water current using artificial neural networks: A case study of the commodore channel

2020 ◽  
Vol 39 (3) ◽  
pp. 942-952
Author(s):  
O.T. Badejo ◽  
O.T. Jegede ◽  
H.O. Kayode ◽  
O.O. Durodola ◽  
S.O. Akintoye
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Models ◽  
Research Work ◽  
Back Propagation ◽  
Water Current ◽  
Artificial Neural ◽  
Coastal Inlets ◽  
Matlab Programming

Water current modelling and prediction techniques along coastal inlets have attracted growing concern in recent years. This is largely so because water current component continues to be a major contributor to movement of sediments, tracers and pollutants, and to a whole range of offshore applications in engineering, environmental observations, exploration and oceanography. However, most research works are lacking adequate methods for developing precise prediction models along the commodore channel in Lagos State. This research work presents water current prediction using Artificial Neural Networks (ANNs). The Back Propagation (BP) technique with feed forward architecture and optimized training algorithm known as Levenbergq-Marquardt was used to develop a Neural Network Water Current Prediction model-(NNWLM) in a MATLAB programming environment. It was passed through model sensitivity analysis and afterwards tested with data from the Commodore channel (Lagos Lagoon). The result revealed prediction accuracy ranging from 0.012 to 0.045 in terms of Mean Square Error (MSE) and 0.80 to 0.83 in terms of correlation coefficient (R-value). With this high performance, the Neural network developed in this work can be used as a veritable tool for water current prediction along the Commodore channel and in extension a wide variety of coastal engineering and development, covering sediment management program: dredging, sand bypassing, beach-contingency plans, and protection of beaches vulnerable to storm erosion and monitoring and prediction of long-term water current variations in coastal inlets. Keywords: Artificial Neural Network, Commodore Channel, Coastal Inlet, Water Current, Back Propagation.

scholarly journals The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

2019 ◽  
Vol 15 (2) ◽  
pp. 164-172 ◽  
Author(s):  
Ku Mohd Kalkausar Ku Yusof ◽  
Azman Azid ◽  
Muhamad Shirwan Abdullah Sani ◽  
Mohd Saiful Samsudin ◽  
Siti Noor Syuhada Muhammad Amin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Particulate Matter ◽  
Meteorological Factors ◽  
Model Development ◽  
Eastern Region ◽  
Multiple Linear Regressions ◽  
Artificial Neural ◽  
Linear Regressions

The comprehensives of particulate matter studies are needed in predicting future haze occurrences in Malaysia. This paper presents the application of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) in order to recognize the pollutant relationship status over particulate matter (PM10) in eastern region. Eight monitoring studies were used, involving 14 input parameters as independent variables including meteorological factors. In order to investigate the efficiency of ANN and MLR performance, two different weather circumstances were selected; haze and non-haze. The performance evaluation was characterized into two steps. Firstly, two models were developed based on ANN and MLR which denoted as full model, with all parameters (14 variables) were used as the input. SA was used as additional feature to rank the most contributed parameter to PM10 variations in both situations. Next, the model development was evaluated based on selected model, where only significant variables were selected as input. Three mathematical indices were introduced (R2, RMSE and SSE) to compare on both techniques. From the findings, ANN performed better in full and selected model, with both models were completely showed a significant result during hazy and non-hazy. On top of that, UVb and carbon monoxide were both variables that mutually predicted by ANN and MLR during hazy and non-hazy days, respectively. The precise predictions were required in helping any related agency to emphasize on pollutant that essentially contributed to PM10 variations, especially during haze period.

Using Artificial Neural Networks in the Reactor Safety Research

2008 ◽  
Author(s):  
Alexander Kratzsch ◽  
Wolfgang Ka¨stner ◽  
Rainer Hampel
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Heat Dissipation ◽  
Model Development ◽  
Reactor Core ◽  
Differential Pressure ◽  
Reactor Safety ◽  
Safety Research ◽  
Artificial Neural ◽  
Residual Heat

The paper deals with the creation of a differential pressure model with artificial neural networks (ANN). Particular, model development and verification tests are considered. One of the main features in reactor safety research is the safe heat dissipation from the reactor core and the reactor containment of light-water reactors. In the case of loss of coolant accident (LOCA) the possibility of the entry of isolation material into the reactor containment and the building sump of the reactor containment and into the associated systems to the residual heat exhaust is a serious problem. This can lead to a handicap of the system functions. To ensure the residual heat exhaust it is necessary the emergency cooling systems to put in operation which transport the water from the sump to the condensation chamber and directly to the reactor pressure vessel. A high allocation of the sieves with fractionated isolation material, in the sump can lead to a blockage of the strainers, inadmissibly increase of differential pressure, build-up at the sieves and to malfunctioning pumps. Hence, the scaling and retention of fractionated isolation material in the building sump of the reactor containment must be estimated. This allows the potential plant status in case of incidents to be assessed. The differential pressure is the essential parameter for the assessment of allocation of the strainers. For modelling we use artificial neural networks. To build up the ANN, the available experimental data are used to train the ANN.

scholarly journals Development and Application of Back-Propagation-Based Artificial Neural Network Models in Solving Engineering

2002 ◽  
Vol 7 (1) ◽  
pp. 55
Author(s):  
Saleh Mohammed Al-Alawi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Model Development ◽  
Back Propagation ◽  
Network Models ◽  
Research Area ◽  
Ann Model ◽  
Neural Network Models ◽  
Fields Of Study ◽  
Artificial Neural

Artificial Neural Networks (ANNs) are computer software programs that mimic the human brain's ability to classify patterns or to make forecasts or decisions based on past experience.  The development of this research area can be attributed to two factors, sufficient computer power to begin practical ANN-based research in the late 1970s and the development of back-propagation in 1986 that enabled ANN models to solve everyday business, scientific, and industrial problems.  Since then, significant applications have been implemented in several fields of study, and many useful intelligent applications and systems have been developed.  The objective of this paper is to generate awareness and to encourage applications development using artificial intelligence-based systems.  Therefore, this paper provides basic ANN concepts, outlines steps used for ANN model development, and lists examples of engineering applications based on the use of the back-propagation paradigm conducted in Oman.  The paper is intended to provide guidelines and necessary references and resources for novice individuals interested in conducting research in engineering or other fields of study using back-propagation artificial neural networks.      

A Novel Prediction Perspective to the Bending Over Sheave Fatigue Lifetime of Steel Wire Ropes by Means of Artificial Neural Networks

2022 ◽  
pp. 648-667
Author(s):  
Tuğba Özge Onur ◽  
Yusuf Aytaç Onur
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fatigue Testing ◽  
Steel Wire ◽  
Experimental Studies ◽  
Fatigue Lifetime ◽  
Wire Ropes ◽  
Long Time ◽  
Significant Case ◽  
Artificial Neural

Steel wire ropes are frequently subjected to dynamic reciprocal bending movement over sheaves or drums in cranes, elevators, mine hoists, and aerial ropeways. This kind of movement initiates fatigue damage on the ropes. It is a quite significant case to know bending cycles to failure of rope in service, which is also known as bending over sheave fatigue lifetime. It helps to take precautions in the plant in advance and eliminate catastrophic accidents due to the usage of rope when allowable bending cycles are exceeded. To determine the bending fatigue lifetime of ropes, experimental studies are conducted. However, bending over sheave fatigue testing in laboratory environments require high initial preparation cost and a long time to finalize the experiments. Due to those reasons, this chapter focuses on a novel prediction perspective to the bending over sheave fatigue lifetime of steel wire ropes by means of artificial neural networks.

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